High dynamic range (HDR) imaging has gained increasing popularity for its ability to faithfully reproduce the luminance levels in natural scenes. Accordingly, HDR image quality assessment (IQA) is crucial but has been superficially treated. The majority of existing IQA models are developed for and calibrated against low dynamic range (LDR) images, which have been shown to be poorly correlated with human perception of HDR image quality. In this work, we propose a family of HDR IQA models by transferring the recent advances in LDR IQA. The key step in our approach is to specify a simple inverse display model that decomposes an HDR image to a set of LDR images with different exposures, which will be assessed by existing LDR quality models. The local quality scores of each exposure are then aggregated with the help of a simple well-exposedness measure into a global quality score for each exposure, which will be further weighted across exposures to obtain the overall quality score. When assessing LDR images, the proposed HDR quality models reduce gracefully to the original LDR ones with the same performance. Experiments on four human-rated HDR image datasets demonstrate that our HDR quality models are consistently better than existing IQA methods, including the HDR-VDP family. Moreover, we demonstrate their strengths in perceptual optimization of HDR novel view synthesis.

翻译：高动态范围（HDR）成像因其能忠实再现自然场景中的亮度水平而日益受到青睐。因此，HDR图像质量评估（IQA）至关重要，但以往研究对此处理较为浅显。现有大多数IQA模型是针对低动态范围（LDR）图像开发并校准的，研究表明这些模型与人类对HDR图像质量的感知相关性较差。在本工作中，我们通过迁移LDR IQA领域的最新进展，提出了一系列HDR IQA模型。方法的关键步骤是设定一个简单的逆显示模型，将HDR图像分解为具有不同曝光度的LDR图像集，并由现有LDR质量模型对其进行评估。随后，借助简单的曝光良好度量，将每个曝光度下的局部质量评分聚合为该曝光度的全局质量评分，再对各曝光度的评分进行跨曝光度加权，从而得到整体质量评分。评估LDR图像时，所提出的HDR质量模型能平滑退化至原始LDR模型，且性能保持不变。在四个人工评分的HDR图像数据集上的实验表明，我们的HDR质量模型始终优于现有IQA方法，包括HDR-VDP系列。此外，我们展示了这些模型在HDR新视角合成感知优化中的优势。